This document discusses automated trading and the role of computer science. It describes how traders observe markets to detect patterns and build prediction models. A system is proposed that learns patterns automatically using machine learning. Computer science helps by developing very reactive systems using algorithms to efficiently process high-dimensional market data and make objective trading decisions. Challenges include developing accurate prediction models that address overfitting, non-analytical objective functions, and reducing the dimensionality of order book data to determine a fair price. The document outlines how computer science areas like networks, operating systems, hardware, data management, and design practices help build high-performance automated trading infrastructure.

1. Automated
Trading

2. What IS Trading /what
do traders do
Traders observe
market behaviour
for a long time
They detect certain patterns in market
data and their mind makes certain
prediction models consciously or
subconsciously ( intuition )
They try to execute trades
as instructed by the abstract
model which was learned by
their mind
Feed historical data
to our system
Make it automatically learn patterns
using ML/Statistics/Pattern Recognition
and build prediction models
Make it execute trades as
instructed by the automated
model which was learned by
our system
How about building a system that learns
patterns in the market automatically?
But, What is the role of
computer science in all
this?

3. Advantages of
automated trading
✖ Very reactive
Quickly Responds to minor changes in the market
✖ Objective in nature
Not subject to emotional bias
✖ Capable of handling very high dimensional
data
1000’s of features

4. Prediction Problem
✖ General Idea
Predict future price under given market conditions/ scenario
✖ Challenge1 - High Dimensional data
1000s of variables/features to
characterise given market scenario.
Question:Why is that a problem ??
Overfitting??
What if most the the features
are correlated ??
How to avoid that ??
Dimensionality
Reduction/PCA/subset
selection

5. Prediction Problem
- Continued
✖ Challenge2 - Loss function/Objective function
How to evaluate if the learned
model/pattern is good enough
Profit??
Accuracy rate??
Correlation ??
What if an objective function is
not analytical.
Other Challenges - Overfitting problems (bias /
variance tradeoff), Outlier detection
What if an objective function is
not even continuous

6. Limit Order Book
Best Bid Price Best Ask Price
{ }Passive
Buy
Orders
Passive
Sell
Orders
Bid
Order
Count
Bid
Size
Bid
Price
Ask
Price
Ask
Size
Ask
Order
Count
4 18 3061 3062 50 6
5 40 3060 3063 100 8
10 200 3059 3065 140 13
12 190 3058 3066 110 7
…. …. …. …. …. ….

7. Bid
Order
Count
Bid
Size
Bid
Price
Ask
Price
Ask
Size
Ask
Order
Count
4 18 3061 3062 50 6
5 40 3060 3063 100 8
10 200 3059 3065 140 13
12 190 3058 3066 110 7
…. …. …. …. …. ….
Place Passive
Sell Orders
Cancel Passive
Sell Orders
Aggressively
Sell
Aggressively
Buy
3 7 2 6 1
8
1
4
Ask Queue
Place Passive
Buy Orders
Cancel Passive
Buy Orders
Bid Queue
7 2 6 3

8. Notion of PRice -
Reducing Order book
Dimensionality✖ No concrete fair price defined
✖ Only order book snapshots and trade
history
✖ Total dimensionality of order book is high
✖ How to map this higher dimension data to
a single number ( A Notion of fair price )
??

9. Let’s take an easy problem
Total size at
Bid
Number of
orders at Bid
Price at
highest Bid
Price at lowest
Ask
Number of
orders at Ask
Total size at
Ask
240 25 99 100 40 80
What is your price ?
● Should it be between the bid and the ask ?
o How about the mean of the two ?
o How should sizes affect it?
● Should the inference be different if the market has been moving a
lot and this is just the current snapshot ?

10. APPLICATION
Of Computer
Science
in ALGORITHMIC
TRADING

11. ➽Every microsecond matters !
➽Need to be Fast, Optimized and Robust.
➽Several Areas of Computer Science involved.
Infrastructure
Challenges

12. Infrastructure
Pipeline
Receiving
&
Decoding
Market Data
Processin
g
&
Building
Book
Strategy
Decision
Sending
Orders
Receiving
Replies

13. Infrastructure
Pipeline
Receiving
&
Decoding
Market Data
Processin
g
&
Building
Book
Strategy
Decision
Sending
Orders
Receiving
Replies
What’s going on in the market right now?

14. Infrastructure
Pipeline
Receiving
&
Decoding
Market Data
Processin
g
&
Building
Book
Strategy
Decision
Sending
Orders
Receiving
Replies
Interpreting market data and effectively
keeping a track of bid/ask sides

15. Infrastructure
Pipeline
Receiving
&
Decoding
Market Data
Processin
g
&
Building
Book
Strategy
Decision
Sending
Orders
Receiving
Replies
In which direction is the market going to
move?

16. Infrastructure
Pipeline
Receiving
&
Decoding
Market Data
Processin
g
&
Building
Book
Strategy
Decision
Sending
Orders
Receiving
Replies
Sending new orders or cancelling
previously placed orders

17. Infrastructure
Pipeline
Receiving
&
Decoding
Market Data
Processin
g
&
Building
Book
Strategy
Decision
Sending
Orders
Receiving
Replies
What happened to my order?
Did it get placed successfully, or was there
any error?

18. ➽Receiving exchange data faster :
UDP(Multicast) or TCP ?
➽Colocation servers, direct lines to exchange.
➽Data through Microwave.
COMPUTER
NETWORKS

19. ➽Book building requires efficient data
structures.
➽Using the best algorithms, minimizing
latency.
➽Optimizing code for better hardware
utilization
Data Structures /
Algorithms

20. ➽Inter Process Communication :
Shared Memory or TCP or Multicast ?
➽Multithreaded Applications:
Locks, Mutexes, Semaphores ?
➽Context Switches ?
 Affine process to a single core
 Messaging Accelerators ( eg. VMA ) :
bypass kernel for network input
Operating Systems

21. ➽Decoding provided data using Hardware
(instead of software)?
➽FPGAs programmed to decode data.
➽Latency benefits due to parallel processing
(at Hardware level)
Digital Hardware
Design

22. ➽Managing large amounts of historical
exchanges’ data, and running studies on it.
➽Amazon EC2 (compute), S3 (backups)
BIG DATA
MANAGEMENT

23. ➽Large Codebase with common code.
➽Design Patterns, Object Oriented, Templated
Programming.
Design
Practices

24. Thanks!
Any questions?
Two Roads
Technological Solutions
http://www.tworoads.co.in/
careers@tworoads.co.in
08800355641